Urine and serum biomarkers associated with diabetic nephropathy

ABSTRACT

Disclosed is use of urine and serum biomarkers in diagnosing diabetic nephropathy, staging diabetic nephropathy, monitoring diabetic nephropathy progress, and assessing efficacy of diabetic nephropathy treatments. These biomarkers include urine precursor alpha-2-HS-glycoprotein, urine alpha-1 antitrypsin, urine alpha-1 acid glycoprotein, urine osteopontin, serum osteopontin, their fragments, and combinations thereof.

RELATED APPLICATION

This application claims priority to U.S. Provisional Application No.61/147,778, filed on Jan. 28, 2009, the content of which is herebyincorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

Diabetic nephropathy (DN) is a progressive kidney disease associatedwith longstanding diabetes mellitus. It causes abnormal fluid filtrationand increased urinary albumin excretion, eventually leading to kidneyfailure.

DN displays no symptoms in its early course. As such, it is difficult todetect the incipiency of this disease. In fact, present diagnosis of DNdepends on development of microalbuminuria, which occurs when kidneydamage is already in place. The lack of an early diagnostic testprevents effective treatment of early stage DN.

It is of great importance to identify reliable biomarkers useful indiagnosing early stage DN.

SUMMARY OF THE INVENTION

The present invention is based on unexpected discoveries that a numberof urine and serum proteins and their fragments, either alone or incombination, are differentially presented in DN patients as compared toDN-free subjects. These protein molecules are therefore useful markersfor diagnosing early stage DN.

Accordingly, one aspect of this invention features a method ofdiagnosing DN in a subject. This method includes at least two steps: (i)determining in a subject suspected of having DN a level of a biomarker,and (ii) assessing whether the subject has DN based on the level of thebiomarker. An increase in the level of the biomarker, as compared tothat in a DN-free subject, indicates that the subject has DN.

The biomarker used in this diagnostic method is one of the four proteinmolecules listed below:

(i) a first urine protein molecule that is precursoralpha-2-HS-glycoprotein or a fragment thereof having at least ten aminoacid residues, such as, mature alpha-2-HS-glycoprotein,VVSLGSPSGEVSHPRKT (SEQ ID NO:1), or MGVVSLGSPSGEVSHPRKT (SEQ ID NO:2);

(ii) a second urine protein molecule that is alpha-1 antitrypsin or afragment thereof having at least ten amino acid residues, such asKGKWERPFEVKDTEEEDF (SEQ ID NO:3); MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:4),EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO:5), orEDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6);

(iii) a third urine protein molecule that is a fragment of alpha-1 acidglycoprotein having at least ten amino acid residues, such asGQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID NO:7); and

(iv) a serum protein molecule that is osteopontin or a fragment thereofhaving at least ten amino acid residues, such asYPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO:8) orKYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO:9).

The diagnostic method described above can further include, after theassessing step, a step of correlating the biomarker level with the DNstatus (i.e., whether it is at early or late stage). When the biomarkeris protein molecules (i) or (iv), an increase in its level relative tothat in a DN-free subject is indicative of late stage DN. For abiomarker that is protein molecules (ii) or (iii), its level indicatesthe DN status when compared with pre-determined reference biomarkerlevels representing early and late stage DN.

In another aspect, the present invention features a method for assessingefficacy of a DN treatment in a subject (e.g., a human patient or alaboratory animal). This method includes determining in the subjectpre-treatment and post-treatment levels of protein molecules (i), (ii),(iii), or (iv), and assessing efficacy of the treatment based on achange in the level of the biomarker after the treatment. If thepost-treatment level of the biomarker remains the same or decreases ascompared to the pre-treatment level of the biomarker, it indicates thatthe treatment is effective.

In yet another aspect, this invention features a method for determininga DN stage, including at least four steps: (i) obtaining a urine sampleand optionally, a serum sample from a subject suspected of havingdiabetic nephropathy, (ii) determining in the sample(s) a level of oneof the biomarkers listed in the preceding paragraph, (iii) calculating adisease score based on the level of the biomarker, and (iv) assessingthe subject's diabetic nephropathy stage based on the disease score ascompared to pre-determined cutoff values indicating different diabeticnephropathy stages. In this method, the calculating step can beperformed by ridge regression analysis, factor analysis, discriminantfunction analysis, and logistic regression analysis.

The biomarker used in the just-described DN staging method is composedof at least two of the following five protein molecules: proteinmolecules (i)-(iv) listed above and protein molecule (v) that is urineosteopontin or its fragment described above. In one example, thebiomarker is composed of all of the five protein molecules. In anotherexample, it is composed of at least two of protein molecules (i)-(iii)and (v).

Alternatively, the biomarker is composed of at least two of the fiveprotein molecules listed above and additionally, one or more clinicalfactors, e.g., age, gender, HbA1c, albuminlcreatinine ratio (ACR), andglomerular filtration rate (GFR).

In still another aspect, the present invention provides a method formonitoring DN progress based on the level of any of the above-mentionedbiomarkers. This method includes obtaining two urine samples andoptionally, two serum samples, within a time span of 2 weeks to 12months (e.g., 2-24 weeks or 3-12 months) from a subject suspected ofhaving DN, determining in the samples a level of one of the biomarkers,calculating disease scores based on the biomarker levels, and assessingDN progress in the subject based on the disease scores. The diseasescore for the later-obtained samples being greater than that for theearlier-obtained samples is indicative of DN exacerbation.

The biomarkers mentioned above can also be used to assess efficacy of aDN treatment. The treatment is effective if the post-treatment level ofone of the biomarkers remains unchanged or decreases as compared to thepre-treatment level of the same biomarker.

The present invention further provides a kit for use in any of themethods described above. This kit includes two, three, or fourantibodies with different antigen specificities. Each of theseantibodies is capable of binding to one of (i) alpha-2-HS-glycoprotein,(ii) alpha-1 antitrypsin, (iii) alpha-1 acid glycoprotein, and (iv)osteopontin. In one example, this kit contains only antibodies specificto antigens to be detected (e.g., biomarkers associated with DN) forpractice one of the methods disclosed herein. Namely, it consistsessentially of such antibodies.

Also within the scope of this invention is an isolated antibodyspecifically binding one of the following peptide:

(SEQ ID NO: 2) MGVVSLGSPSGEVSHPRKT, (SEQ ID NO: 3) KGKWERPFEVKDTEEEDF,(SEQ ID NO: 4) MIEQNTKSPLFMGKVVNPTQK, (SEQ ID NO: 6)EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA, (SEQ ID NO: 7)GQEHFAHLLILRDTKTYMLADVNDEKNWGLS, (SEQ ID NO: 8) YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK, and (SEQ ID NO: 9)KYPDAVATWLNPDPSQKQNLLAPQTLPSK.

The terms “an isolated antibody” used herein refers to an antibodysubstantially free from naturally associated molecules. Morespecifically, a preparation containing the antibody is deemed as “anisolated antibody” when the naturally associated molecules in thepreparation constitute at most 20% by dry weight. Purity can be measuredby any appropriate method, e.g., column chromatography, polyacrylamidegel electrophoresis, and HPLC.

Any of the antibodies described above can be used in manufacturing a kituseful in practicing any of the methods of this invention.

The details of one or more embodiments of the invention are set forth inthe description below. Other features or advantages of the presentinvention will be apparent from the following drawings and detaileddescription of several embodiments, and also from the appended claims.

BRIEF DESCRIPTION OF THE DRAWING

The drawing is first described.

FIG. 1 is a diagram showing boxplots for urine alpha-2-HS-glycoprotein(uDN2; see panel A), urine alpha-1 antitrypsin (uDN5; see panel B),urine alpha-1 acid glycoprotein (uGR3; see panel C), and serumosteopontin (sDNO; see panel D) in various groups of DN patients. Theupper and lower limits of the boxes mark the 25% and 75% values with themedians as the lines across the boxes. The upper whisker marks thelargest value below the upper fence, which is the 75% value plus 1.5interquartile range and the lower whisker marks the smallest value abovethe lower fence, which is the 25% value minus 1.5 interquartile range.

FIG. 2 is a graph showing MALDI-TOF-MS peak intensities from urinesamples of control subjects and diabetic nephropathy (DN) patients.There was a significant increase in the peak intensity of Peak 4(representing a unique peptide) in urine samples of DN patients ascompared to healthy subjects, patients with diabetic mellitus withoutnephropathy (DM), and patients with diabetes and uremia.

FIG. 3 includes a table and a graph showing that the level of anosteopontin (DNO) peptide in DN patients was decreased using isobarictags for relative and absolute quantification (iTRAQ).

FIG. 4 is a set of Western blots obtained using antibodies generatedagainst DN peptide biomarkers. H: healthy subject; DN: diabeticnephropathy patients; DM: diabetic patients without nephropathy.

DETAILED DESCRIPTION OF THE INVENTION

DN is a kidney disorder associated with diabetes. It has fiveprogression phases:

Stage 1: characterized by diabetic mellitus with normal GFR and normalalbuminuria (ACR<30 mg/g);

Stage 2: characterized by glomerular hyperfiltration (greater than 120mL/minute/1.73 m²) and renal enlargement accompanying with normal GFRand normal albuminuria(ACR<30 mg/g);

Stage 3: characterized by microalbuminuria;

Stage 4: characterized by overt albuminuria and a progressive decline inGFR; and

Stage 5: characterized by a GFR of less than 15 mL/minute/1.73 m².Commonly, stages 1-3 are deemed as early stage and stages 4 and 5 aredeemed as late stage.

We have identified a number of biomarkers associated with DN, especiallyDN in different stages. These biomarkers are composed of one or more ofthe following four proteins and their fragments, either in urine or inserum: (a) alpha-2-HS-glycoprotein (GenBank accession no. NP_(—)001613;10 Jan. 2010); (b) alpha-1-antitrypsin (GenBank accession no. AAB59495;10 Jan. 2010); (c) alpha-1 acid glycoprotein (GenBank accession no.EAW87416; 10 Jan. 2010); and

(d) Osteopontin, which includes two isoforms known as secretedphosphoprotein 1a (GenBank accession no. NP_(—)001035147; 17 Jan. 2010)and secreted phosphoprotein 1b (GenBank accession no. NP_(—)000573; 10Jan. 2010,).

The fragments of these four proteins have a minimum length of ten aminoacids and preferably, a maximum length of 190 to 410 amino acids. Forexample, fragments of proteins (a), (b), (c), and (d) can contain up to357, 408, 191, and 290 amino acid residues, respectively.

We have also found that biomarkers composed of one or more of the abovementioned proteins/fragments, and one or more clinical factors (e.g.,age, gender, HbA1c, ACR, and GFR) are also associated with DN indifferent stages.

Accordingly, one aspect of the present invention relates to a DNdiagnostic method using any of the biomarkers described above. Topractice this method, a urine sample and, when necessary, a serumsample, is collected from a subject suspected of having DN and the urineand serum levels of one or more of the four proteins listed above ortheir fragments can be determined via routine methods, e.g., massspectrometry and immune analysis. If applicable, the clinical factorsare determined by route methods.

When a biomarker contains a single protein molecule, its level in asubject can be compared with a reference point to determine whether thatsubject has DN. The reference point, representing the level of the samebiomarker in a DN-free subject, can be determined based on therepresentative levels of the biomarker in groups of DN patients andDN-free subjects. For example, it can be the middle point between themean levels of these two groups. A biomarker level higher than thereference point is indicative of DN.

When a biomarker contains at least two protein molecules and optionally,at least one clinical factor, the levels of the protein molecules andthe value(s) of the clinical factor(s) can be subjected to a suitableanalysis to generate a disease score (e.g., represented by a numericnumber) that characterizes the level of the biomarkers. Examples of theanalysis include, but are not limited to, discriminate functionanalysis, logistic regression analysis, ridge regression analysis,principal component analysis, factor analysis, and generalized linearmodel. The disease score is then compared with a reference pointrepresenting the level of the same biomarker in DN-free subjects. Thereference point can be determined by conventional methods. For example,it can be a score obtained by analyzing the mean levels of the proteinmolecules and when necessary, the mean value(s) of the clinicalfactor(s) in DN-free subjects with the same analysis. The disease scorebeing higher than the reference point is indicative of DN presence.

Another aspect of this invention relates to a method for determining aDN stage based on any of the biomarkers described above. To practicethis method, a biomarker level of a DN patient, preferably representedby a disease score, is compared with a set of pre-determined cutoffvalues that distinguish different DN stages to determine the subject'sDN stage. The cutoff values can be determined by analyzing therepresentative levels of the same biomarker in different-staged DNpatients via the same analysis.

Described below is an exemplary procedure for determining theaforementioned cutoff values based on a biomarker associated with DN indifferent stages:

(1) assigning DN patients to different groups according to their diseaseconditions (e.g., DN stages and risk factors);

(2) determining in each patient group the levels/values of the proteinmolecules and clinical factors constituting the biomarker;

(4) subjecting the protein levels and clinical factor values to asuitable analysis to establish a model (e.g., formula) for calculating adisease score, and

(6) determining a cutoff value for each disease stage based on a diseasescore (e.g., mean value) representing each patient group, as well asother relevant factors, such as sensitivity, specificity, positivepredictive value (PPV) and negative predictive value (NPV).

Any of the models thus generated can be assessed for its diagnosis valueby a receiver-operating characteristic (ROC) analysis to create a ROCcurve. An optimal multivariable model provides a large Area under Curve(AUC) in the ROC analysis. See the models described in Examples 1-3below.

In still another aspect, this invention relates to a method ofmonitoring nephropathy progress in a subject based on any of thebiomarkers described above. More specifically, two urine samples and/orserum samples from a subject can be obtained within a suitable time span(e.g., 2 weeks to 12 months) and examined to determine the levels of oneof the biomarkers. Disease scores are then determined as describedabove. If the disease score representing the biomarker level in thelater obtained sample(s) is higher than that in the earlier-obtainedsample(s), it indicates DN exacerbation in the subject.

The monitoring method can be applied to a human subject suffering fromor at risk for DN. When the human subject is at risk for or in earlystage DN, the level of the biomarker can be examined once every 6 to 12months to monitor DN progress. When the human subject is already in latestage DN, it is preferred that the biomarker level be examined onceevery 3 to 6 months.

The monitoring method described above is also applicable to laboratoryanimals, following routine procedures, to study DN. The term “alaboratory animal” used herein refers to a vertebrate animal commonlyused in animal testing, e.g., mouse, rat, rabbit, cat, dog, pig, andnon-human primate. Preferably, a laboratory animal is examined todetermine the biomarker level once every 2 to 24 weeks.

Any of the biomarkers can also be used to assess efficacy of a DNtreatment in a subject in need (i.e., a human DN patient or a laboratoryanimal bearing DN). In this method, disease scores representing levelsof one of the biomarkers described above are determined before, during,and after the treatment. If the disease scores remain the same ordecline over the course of the treatment, it indicates that thetreatment is effective.

Also disclosed herein is a kit useful in practicing any of theabove-described methods. This kit contains two, three, or fourantibodies with different antigen specificities. Each of theseantibodies is capable of binding to one of (i) alpha-2-HS-glycoprotein,(ii) alpha-1 antitrypsin, (iii) alpha-1 acid glycoprotein, or (iv)osteopontin. The antibodies specific to proteins (i), (ii), (iii), and(iv) can bind to their fragments MGVVSLGSPSGEVSHPRKT (SEQ ID NO:2),KGKWERPFEVKDTEEEDF (SEQ ID NO:3), MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:4),EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6),GQEHFAHLLILRDTKTYMLADVNDEKNWGLS (SEQ ID NO:7),YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO:8), andKYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO:9), i.e., specific to anyantibody epitopes contained in these fragments. In one example, this kitcontains only antibodies specific to antigens to be detected (e.g.,protein molecules associated with DN) for practice one of the methodsdisclosed herein. Namely, the kit consists essentially of suchantibodies.

The kit described above can include two different antibodies (i.e., acoating antibody and a detecting antibody) that bind to the sameantigen. Typically, the detecting antibody is conjugated with a moleculewhich emits a detectable signal either on its own or via binding toanother agent. The term “antibody” used herein refers to a wholeimmunoglobulin or a fragment thereof, such as Fab or F(ab′)₂ thatretains antigen-binding activity. It can be naturally occurring orgenetically engineered (e.g., single-chain antibody, chimeric antibody,or humanized antibody).

The antibodies included in the kit of this invention can be obtainedfrom commercial vendors. Alternatively, they can be prepared byconventional methods. See, for example, Harlow and Lane, (1988)Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory, NewYork. To produce antibodies against a particular biomarker as listedabove, the marker, optionally coupled to a carrier protein (e.g., KLH),can be mixed with an adjuvant, and injected into a host animal.Antibodies produced in the animal can then be purified by affinitychromatography. Commonly employed host animals include rabbits, mice,guinea pigs, and rats. Various adjuvants that can be used to increasethe immunological response depend on the host species and includeFreund's adjuvant (complete and incomplete), mineral gels such asaluminum hydroxide, CpG, surface-active substances such as lysolecithin,pluronic polyols, polyanions, peptides, oil emulsions, keyhole limpethemocyanin, and dinitrophenol. Useful human adjuvants include BCG(bacille Calmette-Guerin) and Corynebacterium parvum. Polyclonalantibodies, i.e., heterogeneous populations of antibody molecules, arepresent in the sera of the immunized animal.

Monoclonal antibodies, i.e., homogeneous populations of antibodymolecules, can be prepared using standard hybridoma technology (see, forexample, Kohler et al. (1975) Nature 256, 495; Kohler et al. (1976) Eur.J. Immunol. 6, 511; Kohler et al. (1976) Eur J Immunol 6, 292; andHammerling et al. (1981) Monoclonal Antibodies and T Cell Hybridomas,Elsevier, N.Y.). In particular, monoclonal antibodies can be obtained byany technique that provides for the production of antibody molecules bycontinuous cell lines in culture such as described in Kohler et al.(1975) Nature 256, 495 and U.S. Pat. No. 4,376,110; the human B-cellhybridoma technique (Kosbor et al. (1983) Immunol Today 4, 72; Cole etal. (1983) Proc. Natl. Acad. Sci. USA 80, 2026, and the EBV-hybridomatechnique (Cole et al. (1983) Monoclonal Antibodies and Cancer Therapy,Alan R. Liss, Inc., pp. 77-96). Such antibodies can be of anyimmunoglobulin class including IgG, IgM, IgE, IgA, IgD, and any subclassthereof. The hybridoma producing the monoclonal antibodies of theinvention may be cultivated in vitro or in vivo. The ability to producehigh titers of monoclonal antibodies in vivo makes it a particularlyuseful method of production.

Moreover, antibody fragments can be generated by known techniques. Forexample, such fragments include, but are not limited to, F(ab′)₂fragments that can be produced by pepsin digestion of an antibodymolecule, and Fab fragments that can be generated by reducing thedisulfide bridges of F(ab′)₂ fragments.

Without further elaboration, it is believed that one skilled in the artcan, based on the above description, utilize the present invention toits fullest extent. The following specific embodiments are, therefore,to be construed as merely illustrative, and not limitative of theremainder of the disclosure in any way whatsoever. All publicationscited herein are incorporated by reference.

EXAMPLE 1 Diagnosing DN Based on Urine Alpha-2-HS-Glycoprotein, UrineAlpha-1 Antitrypsin, Urine Alpha-1 Acid Glycoprotein, or SerumOsteopontin

Material and Methods

(i) Subjects

83 diabetic mellitus patients (designated “DM subjects”), and 82 DNpatients (designated “DN subjects”) were recruited at the Tri-GeneralMilitary Hospital in Taipei, Taiwan, following the standards set forthby the American Diabetic Association and also described below:

DM: suffering from diabetic mellitus but free of DN (see the standardsdescribed below);

DN: suffering from diabetic mellitus and secreting urinary protein at alevel greater than 1 g per day, having DN as proven by biopsy, or havinguremia.

All of the subjects were assigned into a training group and a testinggroup at a ratio of 7:3.

(ii) Sample Collection and Processing

First-morning-void urinary samples and serum samples were collected fromeach of the subjects mentioned above. Peptides contained in the urinesamples were examined by urinary matrix-assisted laserdesorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS)and by isobaric tags for relative and absolute quantification (iTRAQ).

Protein molecules, including alpha-2-HS-glycoprotein (DN2), alpha-1antitrypsin (DN5), osteopontin (DNO), and alpha-1 acid glycoprotein(GR3), were examined to determine their concentrations in both the urineand serum samples by ELISA. Briefly, urine samples were mixed withprotease inhibitors and diluted at 1:100 with a dilution buffer and theserum samples were diluted at 1:10. The diluted samples were placed inELISA plates in triplicates. The levels of DNO, DN2, DN5 and GR3concentration were measured via the standard sandwich ELISA method.

-   -   A 5-parameter standard curve was used for concentration        calculation. Only standards and samples with a coefficient of        variation (CV) of less than 15% were included, and those not        meeting criteria were repeated. The protein levels in the urine        samples were normalized against the creatinine levels in the        same urine samples, which were measured with the Quantichrome        Creatinine Assay (BioAssay Systems, (Hayward)Calif., USA).        (iii) Statistical Analysis

The data indicating the urine and serum protein concentrations of eachexamined protein was statistically analyzed and performed as representedby auROC from 0.44-0.87 in their independent ability to distinguish DNsubjects from DM subjects. For each subject, correlation between valueswas determined by Spearman or Pearson analysis depending on results oftest for normality. Group mean or median comparisons were made with theStudent T-test or the Nonparametric Mann-Whitney Test as appropriate.Statistical significance was obtained when p<0.05. Statistics werepresented either as mean±standard error of mean (SEM) or as median with[25%, 75%].

Results

(i) Patient Characteristics

Tables 1 and 2 below show the characteristics of patients in thetraining group and testing group and those in DM, and DN groups:

TABLE 1 Characteristics of Patients in Training and Testing GroupsTraining Testing (n = 118) (n = 47) P value Age, mean (SD) 59.94 (9.37)60.28 (9.48) 0.8362 Female, n (%) 83 (70) 27 (57) 0.16 MDRD_S_GFR, mean(SD) 86.56 (33.11) 83.05 (43.96) 0.5785 ACR (ug/mg), mean (SD) 737.82(1465.47) 1084.18 (2030.98) 0.2239 Urine TP/Cr (mg/mg), mean (SD) 1.01(2.01) 1 (1.78) 0.9963 Serum Creatinine (mg/dL), mean (SD) 1.02 (0.87)1.34 (1.44) 0.0903 HbA1c (%), mean (SD) 8.49 (1.5) 8.29 (2.19) 0.5356Markers (creatinine-adjusted), mean (SD) uDNO (ng/mg) 1452.71 (1416.7)1488.77 (1222.2) 0.8687 sDNO (ng/ml) 40.65 (34.52) 38.35 (34.13) 0.6926uDN2 (ng/mg) 4225.77 (9279.63) 5999.64 (10305.95) 0.2983 uDN5 (ng/mg)15951.12 (94956.78) 45479.82 (199827.84) 0.3228 uGR3 (ng/mg) 32823.47(62290.96) 42709.23 (103787.54) 0.5333 MDRD_S_GFR: Modification of Dietin Renal Disease-Simplify-Glomerular Filtration Rate (ml/min/1.73 m²)TP/Cr: Total protein/Creatinine

TABLE 2 Characteristics of Patients in DM and DN Groups Training (n =118) Testing (n = 47) DM (n = 61) DN (n = 57) P value DM (n = 22) DN (n= 25) P value Age, mean (SD) 57.11 62.96 0.0006 59.09 61.32 0.4230(8.05) (9.8) (8.82) (10.09) Female, n (%) 43 (70) 40 (70) 1.00 12 (55)15 (60) 0.93 MDRD_S_GFR, mean (SD) 111.21 60.18 <.0001 115.6 54.41<.0001 (15.75) (25.59) (33.66) (29.79) ACR (ug/mg), mean (SD) 11.351515.26 <.0001 9.63 2029.78 0.0004 (6.81) (1815.72) (5.61) (2432.31)Urine TP/Cr (mg/mg), 0.17 1.9 <.0001 0.17 1.7 0.0019 mean (SD) (0.51)(2.56) (0.32) (2.18) Serum Creatinine 0.66 1.42 <.0001 0.67 1.92 0.0019(mg/dL), mean (SD) (0.12) (1.12) (0.15) (1.79) HbA1c (%), mean (SD) 8.348.7 0.2311 8.37 8.22 0.8238 (1.48) (1.53) (1.61) (2.66) Markers(creatinine-adjusted), mean (SD) uDNO (ng/mg) 1422.18 1366.77 0.80831769.54 1516.44 0.5953 (1105.46) (1347.92) (1260.15) (1945.7) sDNO(ng/ml) 29.03 46.17 0.0026 26.2 64.52 0.0010 (19.32) (37.32) (11.53)(50.47) uDN2 (ng/mg) 1968.47 8084.87 0.0013 968.79 7348.69 0.0074(4218.58) (13101.68) (1144.47) (10865.95) uDN5 (ng/mg) 390.24 40036.860.0467 336.21 71802.69 0.1899 (1327.63) (147186.58) (568.08) (264863.27)uGR3 (ng/mg) 3576.06 67470.92 <.0001 2447.77 71693.1 0.0003 (13562.8)(105208.28) (2742.38) (82996.86)

Statistically significant differences in GFR, ACR, protein, and serumcreatinine levels were observed in the DN subjects versus in the DMsubjects. There was no difference in gender distribution among thegroups.

(ii) Protein Molecules Associated with DN

Via urine proteomic analysis, the peptides listed in Table 3 below werefound to be differentially presented in urine samples from the DMsubjects and DN subjects:

TABLE 3 Differentially Presented Urine/Serum Peptides and Proteins inWhich They are Located Corresponding Peptide Sequences ProteinsVVSLGSPSGEVSHPRKT Alpha-2-HS (SEQ ID NO: 1) glycoproteinMGVVSLGSPSGEVSHPRKT (DN2) (SEQ ID NO: 2) KGKWERPFEVKDTEEEDF Alpha-1-(SEQ ID NO: 3) antitrypsin MIEQNTKSPLFMGKVVNPTQK (DN5) (SEQ ID NO: 4)EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO: 5)EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO: 6) YPDAVATWLNPDPSQKQOsteopontin NLLAPQNAVSSEETNDFKQETLPSK (DNO) (SEQ ID NO: 8)GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS Alpha-1 acid (SEQ ID NO: 7)glycoprotein (GR3)

Via ELISA analysis, three urine protein molecules, i.e., uDN2, uGR3, anduDN5, and one serum protein molecule, i.e., sDNO, were found to beassociated with DN. See FIG. 1, panels A-D and Table 2 above. Morespecifically, the levels of uDN2, uDN5, uGR3, and sDNO were found to beelevated in DN subjects as compared with DMs (free of DN), indicatingthat they are reliable markers for DN. Further, the levels of uDN5 anduGR3 in DN subjects exhibiting macroalbuminuria (ACR>300 mg/g) werehigher than those in DN subjects exhibiting microalbuminuria (ACR 30mg/g to 300 mg/g). Macroalbuminuria is an indicator of late stage DN andmicroalbuminuria indicates early stage DN.

EXAMPLE 2 Staging DN Based on a Combination of uDN2, uDN5, uGR3, uDNO,and sDNO

Two Protein Model

The combined levels of two of uDN2, uDN5, uGR3, uDNO, and sDNO in DMsubjects, and DN subjects were subjected to discriminant functionanalysis, logistic regression analysis, and ridge regression analysis.The results from this study indicate that any combination of two of thefive proteins or their fragments can be used as reliable markers fordetermining DN stages.

Shown below is an exemplary two-protein model, i.e., uDN5 and uGR3,including equations for calculating disease scores based on the combinedlevels of these two protein molecules. Also shown below are tables(i.e., Tables 4-9) listing cutoff values, sensitivities, specificities,positive predictive values (PPV) and negative predictive values (NPV),and area under the ROC curve (AUROC) for this two-protein model.

Discriminant Function Analysis:Disease Score=0.3303×log₂ [uDN5](ng/mg)+0.2732×log₂ [uGR3](ng/mg)+5

TABLE 4 Cutoff Values Representing DN Early and Late Stages Indicated byUrine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 11.227 11.691 11.227 11.691Sensitivity (%) 93 93 96 100 Specificity (%) 90 90 77 83 PPV (%) 90 8383 78 NPV (%) 93 96 94 100 AUROC 0.95 0.96 0.98 0.96

TABLE 5 Cutoff Values Representing DN Stages 1-5 Training set (n = 118)Testing set (n = 47) DN-Stage 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 11.066 11.22711.691 14.017 11.066 11.227 11.691 14.017 Sensitivity (%) 75 93 93 75 8496 100 100 Specificity (%) 89 90 90 90 75 77 83 80 PPV (%) 92 90 83 2187 83 78 18 NPV (%) 69 93 96 99 71 94 100 100 AUROC 0.86 0.95 0.96 0.950.9 0.98 0.96 0.91Logistic Regression Analysis:Disease Score=exp(Logit_value)/(1+exp(Logit_value)), in whichLogit_value=−12.5332+0.7197×log₂ [uDN5](ng/mg)+0.4941×log₂ [uGR3](ng/mg)

TABLE 6 Cutoff Values Representing DN Early and Late Stages Indicated byUrine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 0.445 0.676 0.445 0.676Sensitivity (%) 93 93 100 100 Specificity (%) 90 90 82 83 PPV (%) 90 8386 78 NPV (%) 93 96 100 100 AUROC 0.95 0.96 0.98 0.97

TABLE 7 Cutoff Values Representing DN Stages 1-5 Training set (n = 118)Testing set (n = 47) DN-Stage 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 0.383 0.445 0.6760.996 0.383 0.445 0.676 0.996 Sensitivity (%) 75 93 93 75 84 100 100 50Specificity (%) 89 90 90 90 75 82 83 80 PPV (%) 92 90 83 21 87 86 78 10NPV (%) 69 93 96 99 71 100 100 97 AUROC 0.86 0.95 0.96 0.95 0.9 0.980.97 0.88Ridge Regression Analysis:Disease Score=−1.7697+0.1520×log₂ [uDN5](ng/mg)+0.2254×log₂[uGR3](ng/mg)

TABLE 8 Cutoff Values Representing DN Early and Late Stages Indicated byUrine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 2.254 2.606 2.254 2.606Sensitivity (%) 93 93 100 94 Specificity (%) 90 90 77 79 PPV (%) 90 8383 74 NPV (%) 93 96 100 96 AUROC 0.94 0.96 0.98 0.96

TABLE 9 Cutoff Values Representing DN Stages 1-5 Training set (n = 118)Testing set (n = 47) DN-Stage 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 2.185 2.254 2.6064.016 2.185 2.254 2.606 4.016 Sensitivity (%) 75 93 93 75 84 100 94 100Specificity (%) 89 90 90 90 75 77 79 84 PPV (%) 92 90 83 21 87 83 74 22NPV (%) 69 93 96 99 71 100 96 100 AUROC 0.86 0.94 0.96 0.95 0.89 0.980.96 0.91Three Protein Model

The combined levels of three of uDN2, uDN5, uGR3, uDNO, and sDNO in DMsubjects and DN subjects were subjected to discriminant functionanalysis, logistic regression analysis, factor analysis, and ridgeregression analysis. The results indicate that any three-proteincombination can be used as a reliable marker for DN staging.

Shown below is an exemplary three-protein model, i.e., uDN2, uDN5 anduGR3, including equations for calculating disease scores based on thecombined levels of these three protein molecules. Also shown below aretables (i.e., Tables 10-17) listing cutoff values, sensitivities,specificities, PPV, NPV, and AUROC for this three-protein model.

Discriminant Function AnalysisDisease Score=0.3340×log₂ [uDN5](ng/mg)−0.0142×log₂[uDN2](ng/mg)+0.2784×log₂ [uGR3](ng/mg)+5

TABLE 10 Cutoff Values Representing DN Early and Late Stages Indicatedby Urine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 11.190 11.663 11.190 11.663Sensitivity (%) 93 93 96 100 Specificity (%) 90 90 77 83 PPV (%) 90 8383 78 NPV (%) 93 96 94 100 AUROC 0.95 0.96 0.98 0.96

TABLE 11 Cutoff Values Representing DN Stages 1-5 Training set (n = 118)Testing set (n = 47) DN Stage 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 11.064 11.19011.663 13.986 11.064 11.190 11.663 13.986 Sensitivity (%) 75 93 93 75 8496 100 100 Specificity (%) 89 90 90 90 75 77 83 82 PPV (%) 92 90 83 2187 83 78 20 NPV (%) 69 93 96 99 71 94 100 100 AUROC 0.87 0.95 0.96 0.950.9 0.98 0.96 0.91Factor AnalysisDisease Score=0.9190×log₂ [uDN5](ng/mg)+0.6997×log₂[uDN2](ng/mg)+0.9003×log₂ [uGR3](ng/mg)

TABLE 12 Cutoff Values Representing DN Early and Late Stages Indicatedby Urine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 26.356 28.057 26.356 28.057Sensitivity (%) 84 93 88 100 Specificity (%) 90 90 91 86 PPV (%) 89 8392 82 NPV (%) 86 96 87 100 AUROC 0.93 0.95 0.99 0.97

TABLE 13 Cutoff Values Representing DN Stages 1-5 Training set (n = 118)Testing set (n = 47) DN Stage 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 25.669 26.35628.057 36.464 25.669 26.356 28.057 36.464 Sensitivity (%) 68 84 93 75 8488 100 50 Specificity (%) 89 90 90 90 88 91 86 84 PPV (%) 91 89 83 21 9392 82 12 NPV (%) 63 86 96 99 74 87 100 97 AUROC 0.83 0.93 0.95 0.95 0.910.99 0.97 0.86Logistic Regression Analysis:Disease Score=exp(Logit_value)/(1+exp(Logit_value)), in whichLogit_value=−11.2820+0.8810×log₂ [uDN5](ng/mg)−0.3478×log₂[uDN2](ng/mg)+0.5576×log₂ [uGR3](ng/mg)

TABLE 14 Cutoff Values Representing DN Early and Late Stages Indicatedby Urine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 0.462 0.798 0.462 0.798Sensitivity (%) 91 88 96 94 Specificity (%) 90 90 82 83 PPV (%) 90 82 8677 NPV (%) 92 93 95 96 AUROC 0.95 0.96 0.97 0.95

TABLE 15 Cutoff Values Representing DN Stages 1-5 Training set (n = 118)Testing set (n = 47) DN Stage 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 0.361 0.462 0.7980.997 0.361 0.462 0.798 0.997 Sensitivity (%) 75 91 88 75 90 96 94 100Specificity (%) 89 90 90 90 75 82 83 82 PPV (%) 92 90 82 21 88 86 77 20NPV (%) 69 92 93 99 80 95 96 100 AUROC 0.88 0.95 0.96 0.95 0.89 0.970.95 0.93Ridge Regression Analysis:Disease Score=−1.2900+0.1800×log₂ [uDN5](ng/mg)−0.1013×log₂[uDN2](ng/mg)+0.2505×log₂ [uGR3](ng/mg)

TABLE 16 Cutoff Values Representing DN Early and Late Stages Indicatedby Urine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 2.122 2.831 2.122 2.831Sensitivity (%) 95 85 100 94 Specificity (%) 90 90 68 86 PPV (%) 90 8178 81 NPV (%) 95 92 100 96 AUROC 0.95 0.95 0.97 0.95

TABLE 17 Cutoff Values Representing DN Stages 1-5 Training set (n = 118)Testing set (n = 47) DN Stage 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 2.083 2.122 2.8313.943 2.083 2.122 2.831 3.943 Sensitivity (%) 78 95 85 75 87 100 94 100Specificity (%) 89 90 90 90 69 68 86 82 PPV (%) 92 90 81 21 84 78 81 20NPV (%) 71 95 92 99 73 100 96 100 AUROC 0.88 0.95 0.95 0.95 0.89 0.970.95 0.93Four-protein model

The combined levels of four of uDN2, uDN5, uGR3, uDNO, and sDNO in DMsubjects and DN subjects were subjected to discriminant functionanalysis, logistic regression analysis, factor analysis, and ridgeregression analysis. The results indicate that any combination of fourof the five proteins or their fragments can be used as a reliable markerfor determining DN stages.

Shown below is an exemplary four-protein model, i.e., uDN2, uDN5, uGR3,and sDNO, including equations for calculating disease scores based onthe combined levels of these four protein molecules. Also shown beloware tables (i.e., Tables 18-25) listing cutoff values, sensitivities,specificities, PPVs, NPVs, and AUROC for this four-protein model.

Discriminant Function Analysis:

DiseaseScore=0.2972×log₂[uDN5](ng/mg)+0.0159×log₂[uDN2](ng/mg)+0.2014×log₂[uGR3](ng/mg)+0.5688×log₂[sDNO](ng/ml)+5

TABLE 18 Cutoff Values Representing DN Early and Late Stages Indicatedby Urine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 12.945 13.520 12.945 13.520Sensitivity (%) 88 95 96 100 Specificity (%) 90 90 82 86 PPV (%) 89 8386 82 NPV (%) 89 97 95 100 AUROC 0.94 0.96 0.97 0.97

TABLE 19 Cutoff Values Representing DN Stages 1-5 Training set (n = 118)Testing set (n = 47) DN-Stages 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 12.887 12.94513.520 15.560 12.887 12.945 13.520 15.560 Sensitivity (%) 73 88 95 10081 96 100 100 Specificity (%) 89 90 90 90 81 82 86 82 PPV (%) 91 89 8327 89 86 82 20 NPV (%) 67 89 97 100 68 95 100 100 AUROC 0.87 0.94 0.960.97 0.93 0.97 0.97 0.89Factor Analysis:Disease Score=0.9132×log₂ [uDN5](ng/mg)+0.6950×log₂[uDN2](ng/mg)+0.9080×log₂ [uGR3](ng/mg)+0.4549×log₂ [sDNO](ng/ml)

TABLE 20 Cutoff Values Representing DN Early and Late Stages Indicatedby Urine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 28.459 30.095 28.459 30.095Sensitivity (%) 82 93 92 100 Specificity (%) 90 90 91 83 PPV (%) 89 8392 78 NPV (%) 85 96 91 100 AUROC 0.93 0.96 0.99 0.98

TABLE 21 Cutoff Values Representing DN Stages 1-5 Training set (n = 118)Testing set (n = 47) DN-Stage 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 28.347 28.45930.095 38.624 28.347 28.459 30.095 38.624 Sensitivity (%) 67 82 93 75 8192 100 50 Specificity (%) 89 90 90 90 94 91 83 84 PPV (%) 91 89 83 21 9692 78 12 NPV (%) 62 85 96 99 71 91 100 97 AUROC 0.84 0.93 0.96 0.95 0.920.99 0.98 0.86Logistic Regression Analysis:Disease Score=exp(Logit_value)/(1+exp(Logit_value)), in whichLogit_value=−13.7529+0.9460×log₂ [uDN5](ng/mg)−0.3110×log₂[uDN2](ng/mg)+0.4957×log₂ [uGR3](ng/mg)+0.4787×log₂ [sDNO](ng/ml)

TABLE 22 Cutoff Values Representing DN Early and Late Stages Indicatedby Urine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 0.423 0.804 0.423 0.804Sensitivity (%) 91 88 96 100 Specificity (%) 90 90 77 86 PPV (%) 90 8283 82 NPV (%) 92 93 94 100 AUROC 0.96 0.96 0.97 0.96

TABLE 23 Cutoff Values Representing DN States 1-5 Training set (n = 118)Testing set (n = 47) DN-Stages 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 0.341 0.423 0.8040.998 0.341 0.423 0.804 0.998 Sensitivity (%) 75 91 88 75 90 96 100 100Specificity (%) 89 90 90 90 75 77 86 82 PPV (%) 92 90 82 21 88 83 82 20NPV (%) 69 92 93 99 80 94 100 100 AUROC 0.89 0.96 0.96 0.96 0.91 0.970.96 0.9Ridge Regression Analysis:Disease Score=−1.7588+0.1729×log₂ [uDN5](ng/mg)−0.0971×log₂[uDN2](ng/mg)+0.2381×log₂ [uGR3](ng/mg)+0.1312×log₂ [sDNO](ng/ml)

TABLE 24 Cutoff Values Representing DN Early and Late Stages Indicatedby Urine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 2.261 2.854 2.261 2.854Sensitivity (%) 91 85 96 94 Specificity (%) 90 90 77 90 PPV (%) 90 81 8385 NPV (%) 92 92 94 96 AUROC 0.95 0.95 0.97 0.95

TABLE 25 Cutoff Values Representing DN Stages 1-5 Training set (n = 118)Testing set (n = 47) DN-Stage 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 2.079 2.261 2.8543.950 2.079 2.261 2.854 3.950 Sensitivity (%) 77 91 85 75 87 96 94 100Specificity (%) 89 90 90 90 69 77 90 82 PPV (%) 92 90 81 21 84 83 85 20NPV (%) 70 92 92 99 73 94 96 100 AUROC 0.89 0.95 0.95 0.95 0.89 0.970.95 0.93Five Protein Model

The combined levels of uDN2, uDN5, uGR3, uDNO, and sDNO in DM subjectsand DN subjects were subjected to discriminant function analysis,logistic regression analysis, factor analysis, and ridge regressionanalysis. The results indicate that the combination of these fiveproteins or their fragments can be used as a reliable marker fordetermining DN stages.

Shown below are equations for calculating disease scores based on thecombined levels of these five protein molecules, as well as tables(i.e., Tables 26-33) listing cutoff values, sensitivities,specificities, NPVs, PPVs, and AUROC for this five-protein model.

Discriminant Function Analysis:Disease Score=0.2780×log₂ [uDN5](ng/mg)+0.0231×log₂[uDN2](ng/mg)+0.2236×log₂ [uGR3](ng/mg)+0.6043×log2[sDNO](ng/ml)-0.1513×log 2[uDNO](ng/mg)+5

TABLE 26 Cutoff Values Representing DN Early and Late Stages Indicatedby Urine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 11.818 12.164 11.818 12.164Sensitivity (%) 86 98 96 100 Specificity (%) 90 90 86 86 PPV (%) 89 8389 82 NPV (%) 87 99 95 100 AUROC 0.94 0.97 0.98 0.98

TABLE 27 Cutoff Values Representing DN States 1-5 Training set (n = 118)Testing set (n = 47) DN Stages 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 11.766 11.81812.164 14.432 11.766 11.818 12.164 14.432 Sensitivity (%) 73 86 98 10081 96 100 100 Specificity (%) 89 90 90 90 88 86 86 82 PPV (%) 91 89 8327 93 89 82 20 NPV (%) 67 87 99 100 70 95 100 100 AUROC 0.86 0.94 0.970.98 0.94 0.98 0.98 0.91Factor Analysis:Disease Score=0.9117×log₂ [uDN5](ng/mg)+0.6949×log₂[uDN2](ng/mg)+0.9095×log₂ [uGR3](ng/mg)+0.4554×log2[sDNO](ng/ml)+0.0384×log 2[uDNO](ng/mg)

TABLE 28 Cutoff Values Representing DN Early and Late Stages Indicatedby Urine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 29.475 30.541 29.475 30.541Sensitivity (%) 81 93 88 100 Specificity (%) 90 90 91 83 PPV (%) 88 8392 78 NPV (%) 83 96 87 100 AUROC 0.93 0.96 0.99 0.98

TABLE 29 Cutoff Values Representing DN Stages 1-5 Training set (n = 118)Testing set (n = 47) DN Stages 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 28.740 29.47530.541 39.042 28.740 29.475 30.541 39.042 Sensitivity (%) 67 81 93 75 8188 100 50 Specificity (%) 89 90 90 90 94 91 83 84 PPV (%) 91 88 83 21 9692 78 12 NPV (%) 62 83 96 99 71 87 100 97 AUROC 0.84 0.93 0.96 0.95 0.920.99 0.98 0.86Logistic Regression Analysis:Disease Score=exp(Logit_value)/(1+exp(Logit_value)), in whichLogit_value=−11.4318+0.8188×log₂ [uDN5](ng/mg)−0.5376×log₂[uDN2](ng/mg)+0.7561×log₂ [uGR3](ng/mg)+0.3940×log₂[sDNO](ng/ml)-0.1741×log₂ [uDNO](ng/mg)

TABLE 30 Cutoff Values Representing DN Early and Late Stages Indicatedby Urine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 0.436 0.780 0.436 0.780Sensitivity (%) 91 93 96 100 Specificity (%) 90 90 77 86 PPV (%) 90 8383 82 NPV (%) 92 96 94 100 AUROC 0.96 0.96 0.97 0.96

TABLE 31 Cutoff Values Representing DN States 1-5 Training set (n = 118)Testing set (n = 47) DN Stages 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 0.329 0.436 0.7800.997 0.329 0.436 0.780 0.997 Sensitivity (%) 75 91 93 100 90 96 100 100Specificity (%) 89 90 90 90 75 77 86 80 PPV (%) 92 90 83 27 88 83 82 18NPV (%) 69 92 96 100 80 94 100 100 AUROC 0.89 0.96 0.96 0.96 0.91 0.970.96 0.91Ridge Regression Analysis:Disease Score=−1.3112+0.1648×log₂ [uDN5](ng/mg)−0.0968×log₂[uDN2](ng/mg)+0.2468×log₂ [uGR3](ng/mg)+0.1426×log2[sDNO](ng/ml)-0.0552×log 2[uDNO](ng/mg)

TABLE 32 Cutoff Values Representing DN Early and Late Stages Indicatedby Urine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 2.244 2.729 2.244 2.729Sensitivity (%) 91 88 96 100 Specificity (%) 90 90 82 90 PPV (%) 90 8286 86 NPV (%) 92 93 95 100 AUROC 0.95 0.95 0.98 0.97

TABLE 33 Cutoff Values Representing DN Stages 1-5 Training set (n = 118)Testing set (n = 47) DN Stages 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 2.043 2.244 2.7293.913 2.043 2.244 2.729 3.913 Sensitivity (%) 77 91 88 100 87 96 100 100Specificity (%) 89 90 90 90 69 82 90 80 PPV (%) 92 90 82 27 84 86 86 18NPV (%) 70 92 93 100 73 95 100 100 AUROC 0.89 0.95 0.95 0.96 0.9 0.980.97 0.93

EXAMPLE 3 Staging DN Based on a Combination of uDN2, uDN5, uGR3, and Age

Shown below are equations for calculating disease scores determined bydiscriminant function analysis, factor analysis, logistic regressionanalysis, and ridge regression analysis, based on the level of abiomarker composed of three protein molecules, i.e., uDN2, uDN5, anduGR3, and one clinical factor, i.e., age. Also shown below are tables(i.e., Tables 34-41) listing cutoff values, sensitivities,specificities, PPVs, NPVs, and AUROC for this model.

Discriminant Function Analysis:Disease Score=0.3342×log₂ [uDN5](ng/mg)−0.0201×log₂[uDN2](ng/mg)+0.2826×log₂ [uGR3](ng/mg)+0.0059×Age(year)+5

TABLE 34 Cutoff Values Representing DN Early and Late Stages Indicatedby Urine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 11.515 12.088 11.515 12.088Sensitivity (%) 93 93 100 100 Specificity (%) 90 90 77 79 PPV (%) 90 8383 75 NPV (%) 93 96 100 100 AUROC 0.95 0.96 0.98 0.97

TABLE 35 Cutoff Values Representing DN Stages 1-5 Training set (n = 118)Testing set (n = 47) DN-Stage 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 11.353 11.51512.088 14.343 11.353 11.515 12.088 14.343 Sensitivity (%) 75 93 93 75 84100 100 100 Specificity (%) 89 90 90 90 75 77 79 80 PPV (%) 92 90 83 2187 83 75 18 NPV (%) 69 93 96 99 71 100 100 100 AUROC 0.87 0.95 0.96 0.950.9 0.98 0.97 0.9Factor Analysis:Disease Score=0.9184×log₂ [uDN5](ng/mg)+0.7006×log₂[uDN2](ng/mg)+0.9005×log₂ [uGR3](ng/mg)+0.1863×Age(year)

TABLE 36 Cutoff Values Representing DN Early and Late Stages Indicatedby Urine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 38.341 40.075 38.341 40.075Sensitivity (%) 82 85 96 100 Specificity (%) 90 90 86 83 PPV (%) 89 8189 78 NPV (%) 85 92 95 100 AUROC 0.93 0.94 0.99 0.98

TABLE 37 Cutoff Values Representing DN Stages 1-5 Training set (n = 118)Testing set (n = 47) DN-Stages 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Number of patients (%) 73(62) 57 (48) 41 (35) 4 (3) 31 (66) 25 (53) 18 (38) 2 (4) Cut-off 38.34138.341 40.075 48.538 38.341 38.341 40.075 48.538 Sensitivity (%) 66 8285 50 81 96 100 50 Specificity (%) 89 90 90 90 88 86 83 89 PPV (%) 91 8981 15 93 89 78 17 NPV (%) 62 85 92 98 70 95 100 98 AUROC 0.82 0.93 0.940.91 0.9 0.99 0.98 0.77Logistic Regression Analysis:Disease Score=exp(Logit_value)/(1+exp(Logit_value)), in whichLogit_value=−15.9748+0.8688×log₂ [uDN5](ng/mg)−0.4966×log₂[uDN2](ng/mg)+0.6436×log₂ [uGR3](ng/mg)+0.0879×Age(year)

TABLE 38 Cutoff Values Representing DN Early and Late Stages Indicatedby Urine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 0.321 0.889 0.321 0.889Sensitivity (%) 93 80 100 94 Specificity (%) 90 90 77 83 PPV (%) 90 8083 77 NPV (%) 93 90 100 96 AUROC 0.96 0.95 0.97 0.95

TABLE 39 Cutoff Values Representing DN Stages 1-5 Training set (n = 118)Testing set (n = 47) DN-Stages 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 0.301 0.321 0.8890.997 0.301 0.321 0.889 0.997 Sensitivity (%) 75 93 80 75 87 100 94 100Specificity (%) 89 90 90 90 75 77 83 89 PPV (%) 92 90 80 21 87 83 77 29NPV (%) 69 93 90 99 75 100 96 100 AUROC 0.89 0.96 0.95 0.92 0.88 0.970.95 0.91Ridge Regression Analysis:Disease Score=−2.1690+0.1771×log₂ [uDN5](ng/mg)−0.1074×log₂[uDN2](ng/mg)+0.2474×log₂ [uGR3](ng/mg)+0.0168×Age(year)

TABLE 40 Cutoff Values Representing DN Early and Late Stages Indicatedby Urine Albumin Levels Training set (n = 118) Testing set (n = 47) DM,Micro DM, Micro albuminuria albuminuria vs. vs. Macro Macro DM vs. DNalbuminuria DM vs. DN albuminuria Cut-off 2.139 2.880 2.139 2.880Sensitivity (%) 93 85 100 89 Specificity (%) 90 90 73 83 PPV (%) 90 8181 76 NPV (%) 93 92 100 92 AUROC 0.96 0.95 0.98 0.96

TABLE 41 Cutoff Values Representing DN Stages 1-5 Training set (n = 118)Testing set (n = 47) DN-Stage 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs.5 1 vs. 2-5 1-2 vs. 3-5 1-3 vs. 4-5 1-4 vs. 5 Cut-off 2.128 2.139 2.8804.051 2.128 2.139 2.880 4.051 Sensitivity (%) 75 93 85 75 84 100 89 100Specificity (%) 89 90 90 90 69 73 83 89 PPV (%) 92 90 81 21 84 81 76 29NPV (%) 69 93 92 99 69 100 92 100 AUROC 0.89 0.96 0.95 0.92 0.89 0.980.96 0.92

EXAMPLE 4 Initial identification of DN biomarkers

Midstream urinary specimens in the early morning were obtained from 22healthy subjects without diabetic mellitus and with normal renalfunction, 44 patients with type 2 diabetic mellitus (DM), 48 patientswith diabetic nephropathy (DN), and 20 patients with DN-caused-uremia.These urine samples were treated and analyzed by MALDI-TOF-MS.

Six significant peptides highly associated with DN were identifiedindividually (for example, Peak 4 corresponding to a peptide is shown inFIG. 2). These peptides were identified as fragments of proteins DN2 andDN5. See Table 3 above.

Clinical samples were also collected from 5 healthy individuals, 5 DMpatients with normal renal function, and 5 individuals with Dnmanifesting microalbuminuria. Samples were examined with iTRAQ andseparated by LC-MS/MS. Proteins with levels that significantly differedfrom the group were selected and further analyzed. Two additional Dncandidate biomarkers, i.e., DNO and annexin A2 (DNA), were identified.

Decreases of urinary DNO level and urinary DNA level were apparent inindividuals with DN. An increase of serum DNO can also be used todiagnose DN. Unique peptides from each of these two protein biomarkerswere also identified to be of diagnostic value for DN. See, e.g., FIG.3. The sequence of a DNO fragment, i.e., SEQ ID NO:8, is shown in Table3 above.

Polyclonal and monoclonal antibodies were generated against theabove-described peptides. Urinary samples were collected fromindividuals with varying degrees of renal impairment identified bystandard clinical parameters including albuminuria, serum creatininelevel, and GFR. Urinary values were normalized by urine creatinineconcentration. Western blot analysis was carried out using peptide-basedantibodies. Significant decreases of urinary DNO level and DNA level,and significant increases of urinary DN2level, urinary DN5 level, andserum DNO level were noted in samples from DN patients as compared tohealthy and DM controls. See FIG. 4.

Other Embodiments

All of the features disclosed in this specification may be combined inany combination. Each feature disclosed in this specification may bereplaced by an alternative feature serving the same, equivalent, orsimilar purpose. Thus, unless expressly stated otherwise, each featuredisclosed is only an example of a generic series of equivalent orsimilar features.

From the above description, one skilled in the art can easily ascertainthe essential characteristics of the present invention, and withoutdeparting from the spirit and scope thereof, can make various changesand modifications of the invention to adapt it to various usages andconditions. Thus, other embodiments are also within the claims.

What is claimed is:
 1. A method of diagnosing diabetic nephropathy in asubject, comprising: obtaining a urine sample from a subject suspectedof having diabetic nephropathy; determining the level of a biomarker inthe urine sample, wherein the biomarker is a fragment of alpha-1antitrypsin selected from the group consisting of (i) KGKWERPFEVKDTEEEDF(SEQ ID NO:3), (ii) MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:4), (iii)EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO:5), and (iv)EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6); and assessing whetherthe subject has diabetic nephropathy based on the level of thebiomarker, wherein a higher level of the biomarker, as compared to thatin a diabetic nephropathy-free subject, indicates that the subject hasdiabetic nephropathy, the level of the biomarker being determined withan antibody that specifically binds to the biomarker.
 2. The method ofclaim 1, wherein the fragment of alpha-1 antitrypsin isKGKWERPFEVKDTEEEDF (SEQ ID NO:3).
 3. The method of claim 1, wherein thefragment of alpha-1 antitrypsin is MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:4).4. The method of claim 1, wherein the fragment of alpha-1 antitrypsin isEDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO:5).
 5. The method of claim1, wherein the fragment of alpha-1 antitrypsin isEDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6).
 6. The method of claim1, further comprising, after the assessing step, correlating the levelof the biomarker with diabetic nephropathy status based onpre-determined reference levels of the biomarker representing early andlate stage diabetic nephropathy.
 7. A method for determining diabeticnephropathy stage in a subject, comprising: obtaining a urine sample,and optionally, a serum sample, from a subject suspected of havingdiabetic nephropathy; determining the level of each of a plurality ofbiomarkers in the sample(s), and optionally, one or more clinicalfactors, the one or more clinical factors being selected from the groupconsisting of age, gender, HbA1c, albumin/creatinine ratio, andglomerular filtration rate, wherein the plurality of biomarkers include:(a) a first urine biomarker that is a fragment of alpha-1 antitrypsinselected from the group consisting of (i) KGKWERPFEVKDTEEEDF (SEQ IDNO:3), (ii) MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:4), (iii)EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO:5), and (iv)EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6); and (b) one or morebiomarkers selected from the group consisting of (i) a second urinebiomarker that is precursor alpha-2-HS-glycoprotein, VVSLGSPSGEVSHPRKT(SEQ ID NO:1), or MGVVSLGSPSGEVSHPRKT (SEQ ID NO:2), (ii) a third urinebiomarker that is alpha-1 acid glycoprotein orGQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID NO:7), (iii) a fourth urinebiomarker that is osteopontin orYPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO:8), and (iv) ifthe optional serum sample is obtained, a serum biomarker that isosteopontin or YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO:8);calculating a disease score based on the levels of the plurality ofbiomarkers, and optionally, the one or more clinical factors; andassessing the diabetic nephropathy stage of the subject based on thedisease score as compared to pre-determined cutoff values that indicatedifferent diabetic nephropathy stages, wherein the levels of theplurality of biomarkers are determined with antibodies that specificallybind to the biomarkers.
 8. The method of claim 7, wherein the diseasescore is calculated by an analysis selected from the group consisting ofridge regression analysis, factor analysis, discriminant functionanalysis, and logistic regression analysis.
 9. The method of claim 7,wherein the plurality of biomarkers include the third urine biomarker.10. The method of claim 7, wherein the plurality of biomarkers includethe second urine biomarker and the third urine biomarker.
 11. The methodof claim 7, wherein the plurality of biomarkers include the second urinebiomarker, the third urine biomarker, and the serum biomarker.
 12. Themethod of claim 7, wherein the plurality of biomarkers include thesecond urine biomarker, the third urine biomarker, the fourth urinebiomarker, and the serum biomarker.
 13. The method of claim 7, whereinthe disease score is calculated based on one or more clinical factors.14. The method of claim 7, wherein the fragment of alpha-1 antitrypsinis KGKWERPFEVKDTEEEDF.
 15. A method for monitoring diabetic nephropathyprogress in a subject, comprising: obtaining a first urine sample, andoptionally, a first serum sample, from a subject suspected of havingdiabetic nephropathy; obtaining a second urine sample, and optionally, asecond serum sample, 2 weeks to 12 months later; determining the levelof each of a plurality of biomarkers in the first and second samples,and optionally, one or more clinical factors, the one or more clinicalfactors being selected from the group consisting of age, gender, HbA1 c,albumin/creatinine ratio, and glomerular filtration rate, wherein theplurality of biomarkers include: (a) a first urine biomarker that is afragment of alpha-1 antitrypsin selected from the group consisting of(i) KGKWERPFEVKDTEEEDF (SEQ ID NO:3), (ii) MIEQNTKSPLFMGKVVNPTQK (SEQ IDNO:4), (iii) EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO:5), and (iv)EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6); and (b) one or morebiomarkers selected from the group consisting of (i) a second urinebiomarker that is precursor alpha-2-HS-glycoprotein, VVSLGSPSGEVSHPRKT(SEQ ID NO:1), or MGVVSLGSPSGEVSHPRKT (SEQ ID NO:2), (ii) a third urinebiomarker that is alpha-1 acid glycoprotein orGQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID NO:7), (iii) a fourth urinebiomarker that is osteopontin orYPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO:8), and (iv) ifthe optional first and second serum samples are obtained, a serumbiomarker that is osteopontin orYPDAVATWLNPDPSQKQNLLAPQNAVSSETNDFKQETLPSK (SEQ ID NO:8); calculating afirst disease score and a second disease score based on the levels ofthe plurality of biomarkers in the first and second samples,respectively, and optionally, on the one or more clinical factors; andassessing disease progress in the subject, wherein the second diseasescore being greater than the first disease score is indicative ofdiabetic nephropathy exacerbation, wherein the levels of the pluralityof biomarkers are determined with antibodies that specifically bind tothe biomarkers.
 16. The method of claim 15, wherein the disease score iscalculated by an analysis selected from the group consisting of ridgeregression analysis, factor analysis, discriminant function analysis,and logistic regression analysis.
 17. The method of claim 15, whereinthe plurality of biomarkers include the third urine biomarker.
 18. Themethod of claim 15, wherein the plurality of biomarkers include thesecond urine biomarker and the third urine biomarker.
 19. The method ofclaim 15, wherein the plurality of biomarkers include the second urinebiomarker, the third urine biomarker, and the serum biomarker.
 20. Themethod of claim 15, wherein the plurality of biomarkers include thesecond urine biomarker, the third urine biomarker, the fourth urinebiomarker, and the serum biomarker.
 21. The method of claim 15, whereinthe disease score is calculated based on one or more clinical factors.22. The method of claim 15, wherein the fragment of alpha-1 antitrypsinis KGKWERPFEVKDTEEEDF.